In this work we present a new hybrid cellular genetic algorithm. We take MOCell as starting point, a multi-objective cellular genetic algorithm, and, instead of using the typical genetic crossover and mutation operators, they are replaced by the reproductive operators used in differential evolution. An external archive is used to store the nondominated solutions found during the search process ...

In this paper, based on Magnetotactic Bacteria Optimization Algorithm(MBOA), magnetotactic bacterium multi-objective optimization algorithm (MBMOA) is proposed for solving multi-objective optimization problems(MOPs). Magnetotactic bacterium optimization algorithm is a novel random research algorithm which simulates the process of magnetotactic bacteria (MTB) producing magnetosomes(MTS) to regul...

Serotype M1 group A Streptococcus, the most common cause of invasive disease in many case series, generally have resisted extensive molecular subtyping by standard techniques (e.g., multilocus enzyme electrophoresis, pulsed-field gel electrophoresis). We used automated sequencing of the sic gene encoding streptococcal inhibitor of complement and of a region of the chromosome with direct repeat ...

This paper discusses how preference information of the decision maker can in general be integrated into multiobjective search. The main idea is to first define the optimization goal in terms of a binary performance measure (indicator) and then to directly use this measure in the selection process. To this end, we propose a general indicator-based evolutionary algorithm (IBEA) that can be combin...

This paper introduces a new cellular genetic algorithm for solving multiobjective continuous optimization problems. Our approach is characterized by using an external archive to store nondominated solutions and a feedback mechanism in which solutions from this archive randomly replace existing individuals in the population after each iteration. The result is a simple and elitist algorithm calle...

In this paper, based on Magnetotactic Bacteria Optimization Algorithm (MBOA), magnetotactic bacterium multi-objective optimization algorithm (MBMOA) is proposed for solving multi-objective optimization problems (MOPs). Magnetotactic bacterium optimization algorithm is a novel random research algorithm which simulate the process of magnetotactic bacteria (MTB) producing magnetosomes(MTS) to regu...

Multi-objective as well as dynamic characteristics appear in many real-world problems. In order to use multi-objective evolutionary optimization algorithms (MOEA) efficiently, a systematic analysis of the algorithms’ behavior in dynamic environments by means of dynamic test functions is necessary. These functions can be classified into problems with changing Pareto sets and/or Pareto fronts wit...

Many algorithms for multiobjective optimization have been proposed in the last years. In the recent past a great importance have the MOEAs able to solve problems with more than two objectives and with a large number of decision vectors (space dimensions). The difficulties occur when problems with more than three objectives (higher dimensional problems) are considered. In this paper, a new algor...

Pareto Local Search (PLS) is a generalization of the local search algorithms to handle more than one objective. In this paper, two variants of PLS are examined on the multiobjective 0/1 knapsack problems, compared with three well-known multiobjective EA algorithms, namely SPEA, SPEA2 and NSGA2. Furthermore, A Guided Local Search (GLS) based multiobjective optimization algorithm is proposed, the...

This paper reports our experiments to properly handle the multiobjective optimization nature of poetry generation — as defined in Manurung (2003) — as a stochastic search that seeks to produce a text that simultaneously satisfies the properties of grammaticality, meaningfulness, and poeticness. In particular, we employ the SPEA2 Algorithm (Zitzler, Laumanns, and Thiele 2001). Various results sh...